Articles, Autonomous Systems, IIoT, Smart Manufacturing

Big Data and IoT reshaping the Manufacturing Sector

Big Data and the Internet of Things are disruptive technologies that have made their mark in the manufacturing sector and provide companies a competitive advantage. The integration of IoT into big data analytics has enhanced production processes and has met business needs globally.
Margin pressure, the act of increasing or decreasing profits due to market forces or company policy, is a common issue that most manufacturers face nowadays. Therefore, manufacturers use big data to dive into internal and external data to stay ahead of the competition. They use data to stay ahead by making insightful decisions across different sectors ranging from product development to finance to supply chain.
IIoT or Industrial internet of things is a subset of the Internet of Things (IoT). When combined with Big Data analytics, it has helped manufacturers enhance their security, reduce financial risk, eliminate production downtime and increase the quality of processes and products. The aim of effective Industrial Internet strategies is to eliminate organizational, data and system silos while automating the collection of data throughout business operations.
An organization that has a broader or smarter approach to data analysis will experience high operational dividends. To experience this, most organizations give their employees access to a big data certification, so they can master the skills required to harness the power of analytics.
Let’s take a closer look how the manufacturing sector has taken huge steps towards progress with IIoT and big data.

Manufacturing Costing Estimates becomes Faster:

Manufacturing functions are considered internal suppliers by the sales team or by the product management group by several industries. Thanks to this structure, it becomes necessary for the production team to keep the cost estimates ready for the sales and product teams to use in times of tenders and business development cycles.
Hard market dynamics need accurate and quick estimates of product costing. This information could be a deciding factor in whether a company gets or losses an order. In cases like this, companies can combine historical data like customer data and hit rates with a steady IIoT strategy to advice the sales and product teams on tenders, to improve lead quality and turn-around time for customers.

Analysis of Non-Conformance Reports (NCR) are Easier:

Companies have transcended industries to make use of big data. Irrespective of what product or service a company deals with, chances are, big data has helped them make changes in business processes. Manufacturing units are no different. Most manufacturing organizations gather data points in concern to non-conforming events that surface on the factory floor. These data points are called Non-Conformance Reports. An NCR is used as a tool to prevent mistakes and does not let faulty products reach customers. Most of the time, an NCR is practiced when a particular product, technique or process does not meet the required standards.
The non-conformance report is indicative of underlying problems. Multiple NCRs denote a high deficiency in business processes. IIoT technologies along with big data analysis organize NCR data the right way, discover relationships amongst NCRs and predict future non-conformances.

Optimization of plant load:

Sales and Operations Planning processes are an integral part of any manufacturing company. They help executives understand business needs as well as develop a command and control system to integrate present and future business plans. S&OP drive day-to-day operations for long-term business needs while aligning suppliers, customers with manufacturers. The S&OP process can predict a factory’s load forecast over a period of time, which concludes which products a company need to manufacture at which plant. This planning helps companies set a specific plan in place to help them strategize their financial goals.
However, plant loading decision has implications, especially on the operational and financial performance of a company. Data points like historical load, industrial record, completed projects, and customer patterns optimize plant loading.

Easier Operational Monitoring:

Manufacturers show a significant interest in inexpensive sensors used to reduce condition-based monitoring and maintenance in machines. Wireless devices along with big data processing tools make it affordable and easier to mine actual performance data and maintain equipment health.
For example, various machine tools are developed to work within required temperature and vibration ranges. In these cases, active monitoring by sensors will ensure that alerts are sent out when these machines deviate the temperature and vibration parameters. These alerts could help prevent malfunctioning in such cases. Big data integrated into IIoT enhances the overall equipment effectiveness (OEE), reduces equipment failure and keeps maintenance constant to prevent downtime.

Enhanced Supply-Chain Management:

With increased access to real-time supply chain information, it is now easier than ever to discover issues, reduce inventory upkeep and minimize capital requirements. Manufacturers can understand this information through IIoT.
When plants are connected to suppliers, all parties in the supply chain can monitor material flow, interdependencies, and product manufacturing cycle times. Moreover, IIoT-enabled systems are easy to configure for remote monitoring of inventory, location tracking, and reporting of products when they pass through the supply chain. They even gather and feed delivery information into PLM, ERP and other systems.

Safer Work Environment:

Important Performance Indicators (KPIs) for health, safety, and environment (HSE) include data for short and long-term absences, injury and illness rates, and vehicle incidents. These types of data are particularly stored in multiple spreadsheets, systems, and emails that are not properly connected and are taken into consideration during monthly management audits. Lagging indicators have no relational value and most organizations do not perform thorough real problems analyses. A good Industrial Internet and analytics strategy can address these HSE issues.


At the core, IoT is changing maintenance management. The Internet of Things is a rising technology that is redefining the manufacturing sector. Today, most organizations use these devices to keep up their production, minimize risks, and enhance control throughout business operations.